Mark Stevens

Xumin Gao, Wenxin Xue, Callum Lennox et al.

Aphids can cause direct damage and indirect virus transmission to crops. Timely monitoring and control of their populations are thus critical. However, the manual counting of aphids, which is the most common practice, is labor-intensive and time-consuming. Additionally, two of the biggest challenges in aphid counting are that aphids are small objects and their density distributions are varied in different areas of the field. To address these challenges, we proposed a hybrid automatic aphid counting network architecture which integrates the detection network and the density map estimation network. When the distribution density of aphids is low, it utilizes an improved Yolov5 to count aphids. Conversely, when the distribution density of aphids is high, it switches to CSRNet to count aphids. To the best of our knowledge, this is the first framework integrating the detection network and the density map estimation network for counting tasks. Through comparison experiments of counting aphids, it verified that our proposed approach outperforms all other methods in counting aphids. It achieved the lowest MAE and RMSE values for both the standard and high-density aphid datasets: 2.93 and 4.01 (standard), and 34.19 and 38.66 (high-density), respectively. Moreover, the AP of the improved Yolov5 is 5% higher than that of the original Yolov5. Especially for extremely small aphids and densely distributed aphids, the detection performance of the improved Yolov5 is significantly better than the original Yolov5. This work provides an effective early warning caused by aphids in sugar beet fields, offering protection for sugar beet growth and ensuring sugar beet yield. The datasets and project code are released at: https://github.com/JunfengGaolab/Counting-Aphids.

Xumin Gao, Mark Stevens, Grzegorz Cielniak

The current vision-based aphid counting methods in water traps suffer from undercounts caused by occlusions and low visibility arising from dense aggregation of insects and other objects. To address this problem, we propose a novel aphid counting method through interactive stirring actions. We use interactive stirring to alter the distribution of aphids in the yellow water trap and capture a sequence of images which are then used for aphid detection and counting through an optimized small object detection network based on Yolov5. We also propose a counting confidence evaluation system to evaluate the confidence of count-ing results. The final counting result is a weighted sum of the counting results from all sequence images based on the counting confidence. Experimental results show that our proposed aphid detection network significantly outperforms the original Yolov5, with improvements of 33.9% in AP@0.5 and 26.9% in AP@[0.5:0.95] on the aphid test set. In addition, the aphid counting test results using our proposed counting confidence evaluation system show significant improvements over the static counting method, closely aligning with manual counting results.

Xumin Gao, Mark Stevens, Grzegorz Cielniak

Accurate pest population monitoring and tracking their dynamic changes are crucial for precision agriculture decision-making. A common limitation in existing vision-based automatic pest counting research is that models are typically evaluated on datasets with ground truth but deployed in real-world scenarios without assessing the reliability of counting results due to the lack of ground truth. To this end, this paper proposed a method for comprehensively evaluating pest counting confidence in the image, based on information related to counting results and external environmental conditions. First, a pest detection network is used for pest detection and counting, extracting counting result-related information. Then, the pest images undergo image quality assessment, image complexity assessment, and pest distribution uniformity assessment. And the changes in image clarity caused by stirring during image acquisition are quantified by calculating the average gradient magnitude. Notably, we designed a hypothesis-driven multi-factor sensitivity analysis method to select the optimal image quality assessment and image complexity assessment methods. And we proposed an adaptive DBSCAN clustering algorithm for pest distribution uniformity assessment. Finally, the obtained information related to counting results and external environmental conditions is input into a regression model for prediction, resulting in the final pest counting confidence. To the best of our knowledge, this is the first study dedicated to comprehensively evaluating counting confidence in counting tasks, and quantifying the relationship between influencing factors and counting confidence through a model. Experimental results show our method reduces MSE by 31.7% and improves R2 by 15.2% on the pest counting confidence test set, compared to the baseline built primarily on information related to counting results.

Xumin Gao, Mark Stevens, Grzegorz Cielniak

Accurate monitoring of pest population dynamics is crucial for informed decision-making in precision agriculture. Currently, mainstream image-based pest counting methods primarily rely on image processing combined with machine learning or deep learning for pest counting. However, these methods have limitations and struggle to handle situations involving pest occlusion. To address this issue, this paper proposed a robotic stirring method with trajectory optimization and adaptive speed control for accurate pest counting in water traps. First, we developed an automated stirring system for pest counting in yellow water traps based on a robotic arm. Stirring alters the distribution of pests in the yellow water trap, making some of the occluded individuals visible for detection and counting. Then, we investigated the impact of different stirring trajectories on pest counting performance and selected the optimal trajectory for pest counting. Specifically, we designed six representative stirring trajectories, including circle, square, triangle, spiral, four small circles, and random lines, for the robotic arm to stir. And by comparing the overall average counting error and counting confidence of different stirring trajectories across various pest density scenarios, we determined the optimal trajectory. Finally, we proposed a counting confidence-driven closed-loop control system to achieve adaptive-speed stirring. It uses changes in pest counting confidence between consecutive frames as feedback to adjust the stirring speed. To the best of our knowledge, this is the first study dedicated to investigating the effects of different stirring trajectories on object counting in the dynamic liquid environment and to implement adaptive-speed stirring for this type of task. Experimental results show ...